Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver disease worldwide, affecting approximately 32%-38% of the adult population and posing a growing public health burden. MASLD represents a continuous disease spectrum ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH), progressive hepatic fibrosis, cirrhosis, and ultimately hepatocellular carcinoma (HCC). The pathological core of MASLD lies in disruption of hepatic lipid metabolic homeostasis, characterized by an imbalance among de novo lipogenesis, fatty acid β-oxidation, and very-low-density lipoprotein (VLDL)-mediated lipid export. This metabolic disequilibrium subsequently drives inflammatory injury and fibrotic progression. Among the multiple regulatory pathways involved, thyroid hormone (TH) signaling has emerged as a central regulator of hepatic metabolic homeostasis. The liver is a major peripheral target organ of TH action, where TH predominantly exerts its metabolic effects through thyroid hormone receptor β (TRβ). Large-scale epidemiological studies and meta-analyses have demonstrated that hypothyroidism is significantly associated with increased MASLD prevalence, more severe histological injury, and advanced hepatic fibrosis, suggesting that dysregulation of TH signaling may participate throughout the entire MASLD disease spectrum. At the molecular level, TH regulates hepatic lipid metabolism by coordinating suppression of lipogenesis, enhancement of mitochondrial fatty acid oxidation, and promotion of VLDL assembly and secretion through integrated genomic actions of the T3-TRβ axis and non-genomic signaling pathways. Across different stages of MASLD, TH signaling exerts stage-dependent protective effects. In the steatosis stage, TH improves metabolic flexibility by modulating insulin sensitivity, glucose metabolism, and lipid droplet clearance, thereby alleviating early lipotoxic stress. During progression to MASH, TH attenuates inflammatory amplification by improving mitochondrial homeostasis, suppressing activation of the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, and modulating the gut-liver axis microenvironment. In advanced stages, TH signaling influences hepatic stellate cell activation and extracellular matrix deposition, partly through interaction with the transforming growth factor-β (TGF-β)/SMAD pathway, while alterations in intrahepatic TH availability, mediated by dynamic changes in iodothyronine deiodinase 1 (DIO1), contribute to fibrosis progression and hepatocellular dedifferentiation. In hepatocellular carcinoma, coordinated downregulation of TRβ and DIO1 establishes a tumor-associated hypothyroid state that promotes metabolic reprogramming and tumor progression. The clinical relevance of TH signaling in MASLD has been underscored by the recent approval of Resmetirom, a liver-targeted TRβ‑selective agonist, for the treatment of non-cirrhotic MASH with moderate-to-severe fibrosis (F2-F3). This approval represents a landmark transition from mechanistic understanding to metabolism-centered precision therapy in MASLD. Clinical trials have demonstrated that Resmetirom not only improves key histological endpoints, including MASH resolution and fibrosis regression, but also favorably modulates atherogenic lipid profiles, highlighting the therapeutic potential of selectively targeting hepatic TH pathways. This review systematically summarizes the multidimensional regulatory roles of TH across the MASLD disease spectrum and discusses emerging diagnostic and therapeutic implications of TH-based interventions, aiming to inform future mechanistic research and optimize clinical management strategies.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

ObjectiveBy comparing the composition and content of volatile components in raw products， wine-washed products and wine-fried products of Ligusticum sinense cv. Chaxiong rhizome（LSCR）， to investigate the influence of wine processing on the volatile components of LSCR， in order to provide a basis for the development of quality standards for LSCR and its processed products. MethodsElectronic nose was used to identify the odors of LSCR， wine-washed and wine-fried LSCR， and their volatile components were detected by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the relative mass fractions of these components were determined by peak area normalization method. Principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were performed on the obtained sample data by SIMCA 14.1 software， and the differential components of LSCR， wine-washed and wine-fried LSCR were screened according to the variable importance in the projection（VIP） value>1. Pearson correlation analysis was used to explore the relationship between volatile differential flavor components and electronic nose sensors. ResultsElectronic nose detection results showed that there were significant differences in the odors of LSCR， wine-washed and wine-fried LSCR， mainly reflected in the sensors S2， S4， S5， S6， S11， S12， S13. And a total of 62 compounds were identified from LSCR and its wine-processed products， among which 46， 50 and 51 compounds were identified from LSCR， wine-fried and wine-washed LSCR， respectively. There were 21 differential components between the raw products and wine-fried products， of which 10 components were increased and 11 were decreased after processing. There were 20 differential components between the raw products and wine-washed products， of which 11 constituents increased and 9 decreased after processing. There were 17 differential components between the wine-wash products and wine-fried products. Compared with the wine-washed products， the contents of 13 components in the wine-fried products increased， and the contents of 4 components decreased. The increasing trend of the content of phthalides in the wine-washed products was more obvious than that in the wine-fried products， but the content of total volatile components was higher in the wine-fried products than the wine-washed products. Correlation analysis showed that there were different degrees of correlation between the 7 differential sensors of electronic nose and 24 differential volatile components， mainly phthalides and olefins. ConclusionThe odor and the content of volatile components in LSCR changed obviously after wine processing， and n-butylphthalide， Z-butylidenephthalide and E-ligustilide can be used as the candidate differential markers of volatile components in LSCR before and after wine processing.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

Ureaplasma urealyticum (UU) is one of the most commonly occurring pathogens associated with genital tract infections in infertile males, but the impact of seminal UU infection in semen on intrauterine insemination (IUI) outcomes is poorly understood. We collected data from 245 infertile couples who underwent IUI at The First Affiliated Hospital of USTC (Hefei, China) between January 2021 and January 2023. The subjects were classified into two groups according to their UU infection status: the UU-positive group and the UU-negative group. We compared semen parameters, pregnancy outcomes, and neonatal birth outcomes to investigate the impact of UU infection on IUI outcomes. There were no significantly statistical differences in various semen parameters, including semen volume, sperm concentration, total and progressive motility, sperm morphology, leukocyte count, the presence of anti-sperm antibody, and sperm DNA fragmentation index (DFI), between the UU-positive and UU-negative groups of male infertile patients (all P > 0.05). However, the high DNA stainability (HDS) status of sperm differed between the UU-positive and UU-negative groups, suggesting that seminal UU infection may affect sperm nuclear maturation ( P = 0.04). Additionally, there were no significant differences in pregnancy or neonatal birth outcomes between the two groups (all P > 0.05). These results suggest that IUI remains a viable and cost-effective option for infertile couples with UU infection who are facing infertility issues.
Humans ; Male ; Ureaplasma Infections/complications* ; Female ; Infertility, Male/therapy* ; Ureaplasma urealyticum/isolation & purification* ; Pregnancy ; Adult ; Pregnancy Outcome ; Semen Analysis ; Insemination, Artificial ; Semen/microbiology* ; China

To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

Investigating the correlation between micronucleus formation and male infertility has the potential to improve clinical diagnosis and deepen our understanding of pathological progression. Our study enrolled 2252 male patients whose semen was analyzed from March 2023 to July 2023. Their clinical data, including semen parameters and age, were also collected. Genetic analysis was used to determine whether the sex chromosome involved in male infertility was abnormal (including the increase, deletion, and translocation of the X and Y chromosomes), and subsequent semen analysis was conducted for clinical grouping purposes. The participants were categorized into five groups: normozoospermia, asthenozoospermia, oligozoospermia, oligoasthenozoospermia, and azoospermia. Patients were randomly selected for further study; 41 patients with normozoospermia were included in the control group and 117 patients with non-normozoospermia were included in the study group according to the proportions of all enrolled patients. Cytokinesis-block micronucleus (CBMN) screening was conducted through peripheral blood. Statistical analysis was used to determine the differences in micronuclei (MNi) among the groups and the relationships between MNi and clinical data. There was a significant increase in MNi in infertile men, including those with azoospermia, compared with normozoospermic patients, but there was no significant difference between the genetic and nongenetic groups in azoospermic men. The presence of MNi was associated with sperm concentration, progressive sperm motility, immotile spermatozoa, malformed spermatozoa, total sperm count, and total sperm motility. This study underscores the potential utility of MNi as a diagnostic tool and highlights the need for further research to elucidate the underlying mechanisms of male infertility.
Humans ; Male ; Infertility, Male/genetics* ; Adult ; Micronucleus Tests ; Semen Analysis ; Oligospermia/genetics* ; Azoospermia/genetics* ; Chromosome Aberrations ; Sperm Count ; Micronuclei, Chromosome-Defective ; Middle Aged

OBJECTIVES: To explore the measures to improve the follow-up rate of preterm infants after discharge, and to evaluate the effectiveness of these measures using quality improvement methodology. METHODS: The follow-up status of preterm infants discharged from March to May 2017 was used as the baseline before quality improvement, and a specific quality improvement goal for the follow-up rate was proposed. The Pareto chart was used to analyze the causes of follow-up failure, and a key driver diagram was constructed based on the links involved in improving follow-up rate. The causes of failure were analyzed to determine the key links and intervention measures for quality improvement, and the follow-up rate was monitored weekly using a control chart until the quality improvement goal was achieved. RESULTS: The follow-up rate of preterm infants after discharge was 57.92% (117/202) at baseline before quality improvement, and the quality improvement goal was set to increase the follow-up rate of preterm infants from baseline to more than 80% within 12 months. The Pareto chart analysis showed that the main causes of follow-up failure were deficiencies in follow-up file management and irregular follow-up times (33.70%, 31/92), insufficient follow-up education and poor communication (25.00%, 23/92), and the inability to meet the diverse needs of parents (18.48%, 17/92). Based on the key links for quality improvement and the main causes of follow-up failure, the following intervention measures were adopted: (1) strengthen follow-up publicity and education; (2) build a follow-up team; and (3) establish a follow-up platform and system. The control chart indicated that with the implementation of the above intervention measures, the weekly follow-up rate increased to 74.09% (306/413) in July 2017 and 83.09% (511/615) in December 2017, finally achieving the quality improvement goal. During the COVID-19 pandemic, the follow-up rate of preterm infants fluctuated between 23.54% (460/1 954) and 70.97% (1 931/2 721), and subsequently, it returned to pre-pandemic levels starting in February 2023. CONCLUSIONS The application of quality improvement methodology can help to formulate intervention measures based on the main causes of follow-up failure, thereby improving the follow-up rate of preterm infants after discharge. This quality improvement method is feasible and practical and thus holds promise for clinical application.
Humans ; Quality Improvement ; Infant, Premature ; Infant, Newborn ; Patient Discharge ; Follow-Up Studies ; Female ; Male